Germany’s draft law implementing the EU AI Act offers a robust framework but leaves several grey zones unaddressed regarding content governance, copyright, and the rights of creative professionals.

Germany’s Draft AI Act Implementation Law: Insights, Controversies, and Strategic Considerations

by ChatGPT-4o

I. Introduction

Germany’s draft law, formally titled the Gesetz zur Durchführung der KI-Verordnung(Law on the Implementation of the AI Regulation), sets the national legal framework for enacting Regulation (EU) 2024/1689. While the EU AI Act is directly applicable across the Union, member states must appoint responsible authorities, define enforcement mechanisms, and establish national innovation support infrastructures. Germany's proposed implementation is both administrative and strategic—touching on market surveillance, regulatory coherence, enforcement procedures, and innovation acceleration.

II. Surprising, Controversial, and Valuable Provisions

A. Surprising Provisions

1. Bundesnetzagentur as AI Super-Regulator
   The Federal Network Agency (BNetzA), traditionally overseeing telecommunications and energy, is designated as the central market surveillance authority and innovation hub for AI systems. This is unexpected, as one might have anticipated a new AI-specific authority or the Ministry of Justice taking a leading role.

2. Automatic Approval Mechanism for High-Risk AI Testing
   Tests under real-world conditions for high-risk AI systems are deemed approved if no response is issued within 30 days by the authority (BNetzA). This "silence is consent" rule is a notable procedural shortcut.

3. Creation of a UKIM (Unabhängige KI-Marktüberwachungskammer)
   A fully independent "AI Market Surveillance Chamber" will sit within the BNetzA structure. Its governance model is modeled after judicial independence, with no external instructions allowed, and it reports annually to Parliament. This quasi-judicial independence is rare for technical regulators.

4. Inclusion of AI Violations in Whistleblower Law
   Violations of the AI Act are now formally added as grounds for whistleblowing under Germany’s Hinweisgeberschutzgesetz—a strong signal that AI compliance is being treated on par with financial fraud or environmental violations.

B. Controversial Provisions

1. Broad Access to Sensitive Data by Multiple Authorities
   Several regulatory bodies (e.g., BNetzA, BaFin, BSI, and data protection agencies) are allowed to exchange personal data and trade secrets under relatively broad terms. Although aimed at enforcement, this raises data protection and privacy concerns, especially for rights holders and service providers.

2. No New Rights or Obligations for Citizens and Companies
   The draft claims it imposes no new burdens on citizens or companies, arguing that the AI Regulation itself does so. This legal minimalism sidesteps national clarification on areas like content provenance, LLM outputs, or training data disclosures—issues that many stakeholders expected the national law to address.

3. Explicit Limitation on Public Entity Liability
   Article 15(4) of the draft excludes public authorities from being fined for AI violations. This immunizes state actors and potentially undermines enforcement parity, especially where governments deploy AI in sensitive areas like law enforcement, migration, or education.

4. Delayed Implementation Blamed on Government Formation
   The explanatory note excuses the missed August 2025 deadline due to “new government formation.” While perhaps administratively true, it reflects a lack of urgency given the growing societal and economic impact of AI technologies.

C. Valuable Statements and Structural Innovations

1. Hybrid Supervision Approach
   Rather than creating an entirely new bureaucracy, the law adopts a hybrid model, using existing authorities (e.g., BaFin for finance, Landesbehörden for product safety, BNetzA for AI generally). This could reduce duplication and speed up deployment.

2. KoKIVO: National Coordination and Competence Centre for AI
   A central “KoKIVO” unit at BNetzA will provide legal and technical expertise to other regulators, promote consistency in horizontal legal issues, and support the development of AI codes of conduct. This hub model could be emulated by other member states.

3. Legal Basis for Innovation Sandboxes and “KI-Reallabor”
   The law mandates the creation of at least one “KI-Reallabor” (real-world testing lab) with preferential access for SMEs and startups. It also allows for further expansion via executive decrees.

4. Extension to Financial and Crypto Markets
   BaFin is designated as the market surveillance authority for high-risk AI used in banking, insurance, crypto asset services, and even pension systems. The inclusion of such a wide range of financial institutions (including DeFi actors) reflects deep integration of AI into Germany’s financial ecosystem.

5. Coordination with the Cyber Resilience Act (CRA)
   The draft anticipates future alignment with other European legislation, including the CRA. This is significant because many AI systems will fall under both the AI Act and CRA (e.g., connected medical devices or smart infrastructure).

III. Strategic Takeaways for Scholarly Publishers, Rights Owners, Authors, and Creators

When preparing consultation submissions (due October 10, 2025), stakeholders in content-centric sectors—particularly scholarly publishers and creative rights holders—should focus on the following themes:

1. Transparency and Traceability of AI Systems

• Advocate for the implementation law to define clearer standards for transparencyin content generation, dataset provenance, and metadata disclosure—especially for generative AI and AI summarization tools used in research, publishing, and educational settings.

• Push for stronger obligations for downstream users, not just developers, to disclose AI use.

2. Intellectual Property & Training Data Governance

• Request specific reference to copyright-related risks, especially unauthorized use of copyrighted material in training data or AI outputs.

• Recommend explicit clarification on whether AI-generated content falls under existing copyright laws and who bears responsibility (developer, deployer, or user).

3. Alignment with Data Protection and Platform Liability Laws

• Emphasize the need for harmonization with GDPR, DSM Directive, and pending DSA obligations regarding content provenance, data usage, and algorithmic transparency.

• Flag that AI systems used to summarize, translate, or repackage scholarly content may bypass licensing models, affecting rights and monetization.

4. Monitoring and Enforcement in Digital Publishing and Education

• Recommend that digital publishing platforms and educational AI tools be specifically included in the scope of market surveillance.

• Propose that violations involving unauthorized reproduction, manipulation, or erasure of attribution in AI systems be monitored by a specialized unit.

5. Inclusion in Innovation Incentives and “KI-Reallabor”

• Suggest that academic publishers, libraries, and non-profit research institutions be made eligible for participation in “KI-Reallabor” or receive guidance and funding for AI governance implementation.

• Call for priority access for organizations involved in trusted knowledge dissemination and not only profit-driven startups.

6. Need for Sector-Specific Codes of Conduct

• Request development of codes of conduct tailored to publishing, education, and content curation, facilitated by KoKIVO.

• These codes should define responsible AI use, fair attribution practices, and reader transparency standards.

7. Better Complaint and Redress Mechanisms

• Propose an accessible redress mechanism for authors whose works are used or misattributed by AI systems.

• Suggest inclusion of authors' and publishers’ associations in the complaint system design, ensuring rights-holders are adequately represented.

IV. Conclusion

Germany’s draft law implementing the EU AI Act offers a robust institutional framework but leaves several grey zones unaddressed—especially regarding content governance, copyright, and the rights of creative professionals. While its structure is efficient and focused on innovation, it misses the opportunity to anchor stronger safeguards for content creators, educators, and knowledge custodians.

Scholarly publishers and rights holders should respond decisively to the consultation, urging policymakers to close these gaps—before enforcement begins in August 2026 and AI systems become too embedded to regulate retroactively.